Quantitative
estimates of recharge are critical for optimal management of water resources.
The purpose of this study was to evaluate the use of unsaturated-flow
modeling with readily available online data to estimate recharge over
a range of climate (arid – humid), vegetation (crops, shrubs, grasses,
forests), and soils (fine – coarse) on the basis of data from Texas,
US. Long-term simulations were conducted for the period 1961 – 1990
for 13 sites that corresponded to counties near representative meteorological
stations using the 1-D code UNSAT-H. Simulated drainage at the base of
5-meter profiles was equated to recharge. Soil hydraulic properties were
estimated from SSURGO soils data using pedotransfer functions. Point recharge
for each vegetation and soil-profile layering
combination was regionalized using GIS coverages of vegetation and soil
types for each site. Spatially and temporally averaged recharge rates
are more appropriate for water resources management than point estimates
at a single time. Recharge rates for vegetated layered systems ranged
from 0 mm/yr in arid regions to 114 mm/yr in humid regions and were positively
correlated with precipitation (r=0.8), which indicates that long-term
precipitation can be used as a predictor of average recharge rates in
these regions. Simulated recharge rates compared favorably with previous
estimates based on unsaturated and saturated zone field studies and modeling.
Various scenarios were simulated to evaluate sensitivity of model output
to different factors. Simulated average recharge rates for bare sand ranged
from 54 to 720 mm/yr, which are much greater than those simulated for
vegetated layered soil profiles and indicate that climate is not a limiting
factor for recharge. Layering of soil profiles reduced recharge rates
relative to those for monolithic sands by factors of 2 – 10. These
sensitivity analyses illustrate the relative importance of climate, vegetation,
and soils in controlling recharge. Recharge results from this study were
used as input to groundwater models by adjusting recharge with topography
and by including a scaling factor to account for varying permeability
of underlying geologic units. This modeling approach using online data
provides a valuable tool for recharge estimation and can be used to evaluate
changes in recharge in response to climate variability and land-use change.